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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 2 months ago
MMI1020H Big Data Analysis This course introduces students to the art and science of data analysis and provides a set of statistical and econometric tools that are useful in managerial decision
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, guiding and managing a large, diverse, and complex facilities operation; organizing and reorganizing work of teams and service providers as needed to meet service and financial needs, and ensuring
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of education and experience. Experience: Minimum two (2) years of administrative experience in a large organization, including providing support to senior executives and committees. Working with HR/ LR policies
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clusters, GPU-enabled systems, job schedulers (e.g., Slurm), and parallel computing workflows supporting simulations, bioinformatics, machine learning, or large-scale data analysis Experience managing
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Inclusivity and Goodwill (AIM BIG) is a seven-year SSHRC project that will bring together leading researchers, community organizations, and older adults across Canada, the United Kingdom, Ireland, Norway
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. The reason for the emergence is due to theoretical advances in machine learning, availability of big data, and surges in computational capabilities. This 8-week course provides an introductory overview of data
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supportive capacity with Senior Development Officer and Director, Advancement complex or large gifts. Building and strengthening relationships with stakeholders and partners of strategic importance Essential
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skills; Superior planning, budgeting and project management skills; Strong computer skills with advanced proficiency in Word, Excel and Power Point. Other: Proven ability to think strategically/see the big
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of educational services provision (including large-scale consultancies). Proven record in revenue generation and educational program development with diverse constituencies from public, private, and non-profit
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(Nonessential): Experience in a research or academic environment, particularly in handling large and complex scientific datasets. Knowledge of data modeling, schema design, and data architecture best practices